Automating sequence-based detection and genotyping of SNPs from diploid samples

• Published in: Nature genetics Vol. 38; no. 3; pp. 375 - 381
• Main Authors: Stephens, Matthew, Sloan, James S, Robertson, P D, Scheet, Paul, Nickerson, Deborah A
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group 01.03.2006
• Subjects: Algorithms; Automation - methods; Biological and medical sciences; Chromosomes; Diploidy; DNA - genetics; Fluorescence; Fundamental and applied biological sciences. Psychology; Gene amplification; Genetic Variation; Genetics of eukaryotes. Biological and molecular evolution; Genotype; Genotype & phenotype; Physiological aspects; Polymorphism, Single Nucleotide; Reproducibility of Results; Sensitivity and Specificity; Single nucleotide polymorphisms; United States; Detection; Diploidy; Single nucleotide polymorphism
• ISSN: 1061-4036; 1546-1718
• DOI: 10.1038/ng1746

The detection of sequence variation, for which DNA sequencing has emerged as the most sensitive and automated approach, forms the basis of all genetic analysis. Here we describe and illustrate an algorithm that accurately detects and genotypes SNPs from fluorescence-based sequence data. Because the algorithm focuses particularly on detecting SNPs through the identification of heterozygous individuals, it is especially well suited to the detection of SNPs in diploid samples obtained after DNA amplification. It is substantially more accurate than existing approaches and, notably, provides a useful quantitative measure of its confidence in each potential SNP detected and in each genotype called. Calls assigned the highest confidence are sufficiently reliable to remove the need for manual review in several contexts. For example, for sequence data from 47-90 individuals sequenced on both the forward and reverse strands, the highest-confidence calls from our algorithm detected 93% of all SNPs and 100% of high-frequency SNPs, with no false positive SNPs identified and 99.9% genotyping accuracy. This algorithm is implemented in a software package, PolyPhred version 5.0, which is freely available for academic use.